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The Case for Evaluating Causal Models Using Interventional Measures and
  Empirical Data

The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data

11 October 2019
A. Gentzel
Dan Garant
David D. Jensen
    CML
    ELM
ArXivPDFHTML

Papers citing "The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data"

6 / 6 papers shown
Title
Benchmarking Framework for Performance-Evaluation of Causal Inference
  Analysis
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
Y. Shimoni
C. Yanover
Ehud Karavani
Yaara Goldschmidt
CML
34
54
0
14 Feb 2018
Automated versus do-it-yourself methods for causal inference: Lessons
  learned from a data analysis competition
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Vincent Dorie
J. Hill
Uri Shalit
M. Scott
D. Cervone
CML
105
284
0
09 Jul 2017
Bias and high-dimensional adjustment in observational studies of peer
  effects
Bias and high-dimensional adjustment in observational studies of peer effects
Dean Eckles
E. Bakshy
35
61
0
14 Jun 2017
Distinguishing cause from effect using observational data: methods and
  benchmarks
Distinguishing cause from effect using observational data: methods and benchmarks
Joris M. Mooij
J. Peters
Dominik Janzing
Jakob Zscheischler
Bernhard Schölkopf
CML
60
482
0
11 Dec 2014
Causal Discovery with Continuous Additive Noise Models
Causal Discovery with Continuous Additive Noise Models
Jonas Peters
Joris Mooij
Dominik Janzing
Bernhard Schölkopf
CML
84
563
0
26 Sep 2013
Testing Identifiability of Causal Effects
Testing Identifiability of Causal Effects
D. Galles
Judea Pearl
CML
49
90
0
20 Feb 2013
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